SOTAVerified

Depth Completion

The Depth Completion task is a sub-problem of depth estimation. In the sparse-to-dense depth completion problem, one wants to infer the dense depth map of a 3-D scene given an RGB image and its corresponding sparse reconstruction in the form of a sparse depth map obtained either from computational methods such as SfM (Strcuture-from-Motion) or active sensors such as lidar or structured light sensors.

Source: LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery , Unsupervised Depth Completion from Visual Inertial Odometry

Papers

Showing 1–10 of 242 papers

TitleStatusHype
PacGDC: Label-Efficient Generalizable Depth Completion with Projection Ambiguity and ConsistencyCode1
DidSee: Diffusion-Based Depth Completion for Material-Agnostic Robotic Perception and Manipulation—0
DCIRNet: Depth Completion with Iterative Refinement for Dexterous Grasping of Transparent and Reflective Objects—0
SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping—0
HTMNet: A Hybrid Network with Transformer-Mamba Bottleneck Multimodal Fusion for Transparent and Reflective Objects Depth Completion—0
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World—0
Event-Driven Dynamic Scene Depth Completion—0
Depth Anything with Any Prior—0
All-day Depth Completion via Thermal-LiDAR Fusion—0
WonderTurbo: Generating Interactive 3D World in 0.72 Seconds—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SS-S2DMAE178.85—Unverified
2DDPMAE151.86—Unverified
3VOICEDMAE85.05—Unverified
4ScaffNet-FusionNetMAE59.53—Unverified
5KBNetMAE39.8—Unverified
6NLSPNMAE26.74—Unverified